Scalable Amortized GPLVMs for Single Cell Transcriptomics Data

arXiv:2405.03879v1 Announce Type: cross
Abstract: Dimensionality reduction is crucial for analyzing large-scale single-cell RNA-seq data. Gaussian Process Latent Variable Models (GPLVMs) offer an interpretable dimensionality reduction method, but current scalable models lack effectiveness in clustering cell types. We introduce an improved model, the amortized stochastic variational Bayesian GPLVM (BGPLVM), tailored for single-cell RNA-seq with specialized encoder, kernel, and likelihood designs. This model matches the performance of the leading single-cell variational inference (scVI) approach on synthetic and real-world COVID datasets and …

abstract arxiv bayesian clustering cs.lg current data dimensionality process q-bio.gn rna rna-seq scalable scale stat.ap stat.ml stochastic type types